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arXiv · 1702.06247

SAR: Semantic Analysis for Recommendation

Han Xiao·Lian Meng
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Abstract

Recommendation system is a common demand in daily life and matrix completion is a widely adopted technique for this task. However, most matrix completion methods lack semantic interpretation and usually result in weak-semantic recommendations. To this end, this paper proposes a $S$emantic $A$nalysis approach for $R$ecommendation systems $(SAR)$, which applies a two-level hierarchical generative process that assigns semantic properties and categories for user and item. $SAR$ learns semantic representations of users/items merely from user ratings on items, which offers a new path to recommendation by semantic matching with the learned representations. Extensive experiments demonstrate $SAR$ outperforms other state-of-the-art baselines substantially.

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Han Xiao, Lian Meng. 2017-02-21. SAR: Semantic Analysis for Recommendation. https://arxiv.org/abs/1702.06247

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